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Toward a Near-Lossless Image Compression Strategy for the NASA/USGS Landsat Next MissionAs orbiting Earth imaging platforms carry more complex and capable instruments, efficient methods are needed to reduce the time and cost associated with storing and downlinking greater volumes of image data. The upcoming NASA/USGS Landsat Next mission, with an increase in spatial and spectral resolution over previous Landsat missions, is no exception. Landsat Next will produce nearly six times the amount of image data per day over either of the current Landsat 8 or Landsat 9 observatories. Near-lossless compression, where the image after compression is not identical to the original image, allows for the efficient storage and transmission of all image data while meeting the mission’s global coverage, temporal revisit frequency, and science measurement and performance requirements. Although the Landsat user community is understandably cautious about lossy compression, it is possible to constrain the maximum loss, or error, introduced during compression, ensuring that any added error remains within the intrinsic noise level of the instrument. The Consultative Committee for Space Data Systems image compression standard, CCSDS 123.0-B-2, was chosen for the Landsat Next mission because it is an internationally supported standard suited for integration with space hardware, and it allows control over the magnitude and distribution of compression error. Using several proxy datasets as a surrogate for Landsat Next image data, an investigation was performed to determine a preliminary set of parameter values that would keep the added compression error within acceptable limits. The results of these studies demonstrate that near-lossless image compression can be utilized by the Landsat Next instruments to store and downlink all science data without compromising image quality or mission requirements.
Document ID
20250008078
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Rehman S Eon
(Rochester Institute of Technology Rochester, United States)
Craig De Groot
(KBR (United States) Houston, United States)
Jeffrey A Pedelty
(Bay Engineering Innovations )
Aaron Gerace
(Rochester Institute of Technology Rochester, United States)
Matthew Montanaro
(Rochester Institute of Technology Rochester, United States)
Richard K Covington
(The Aerospace Corporation El Segundo, United States)
Amy S DeLisa
(FTS International LLC)
Wen-Ting Hsieh
(Goddard Space Flight Center Greenbelt, United States)
Joy M Henegar-leon
(McCallie Associates Inc )
Douglas J Daniels
(The Aerospace Corporation El Segundo, United States)
Christopher Engebretson
(Earth Resources Observation and Science Center Sioux Falls, United States)
Christopher J Crawford
(Earth Resources Observation and Science Center Sioux Falls, United States)
Thomas R H Holmes
(Goddard Space Flight Center Greenbelt, United States)
Philip Dabney
(Goddard Space Flight Center Greenbelt, United States)
Bruce D Cook
(Goddard Space Flight Center Greenbelt, United States)
Date Acquired
August 6, 2025
Publication Date
July 30, 2025
Publication Information
Publication: Remote Sensing of Environment
Publisher: Elsevier
Volume: 329
ISSN: 0034-4257
e-ISSN: 1879-0704
Subject Category
Earth Resources and Remote Sensing
Funding Number(s)
WBS: 285372.04.01.01
CONTRACT_GRANT: 80NSSC19K1441
CONTRACT_GRANT: 80NSSC23K1014
CONTRACT_GRANT: 80NSSC23K1015
CONTRACT_GRANT: 80GSFC23CA048
CONTRACT_GRANT: 80NSSC24K1205
CONTRACT_GRANT: 80GSFC19D0011
CONTRACT_GRANT: 80GSFC21CA007
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
External Peer Committee
Keywords
Landsat Next
LandIS
near-lossless image compression
CCSDS 123.0-B-2
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